Alexander Schmidt
Papers
10
Total Citations
192
H-Index
6
About
Alexander Schmidt is a researcher whose work sits at the intersection of acoustic signal processing, autonomous systems, and robot audition. His most significant contribution to the field is the LOCATA Challenge Data Corpus (2018), a benchmark dataset for acoustic source localization and tracking that has garnered over 100 citations and become an important standard for evaluating competing algorithms across applications ranging from smart home devices to hearing aids. Schmidt has made particularly notable advances in ego-noise suppression — the challenge of filtering out the mechanical noise autonomous systems generate from their own movements — developing innovative approaches that leverage motor data to guide multichannel dictionary methods and nonnegative matrix factorization techniques. His broader research vision, articulated in "Acoustic Self-Awareness of Autonomous Systems in a World of Sounds," positions acoustic perception as a critical and underappreciated modality for robots and autonomous agents navigating complex real-world environments. Schmidt's career spans an impressive range, from early work on multibody dynamics simulation in the 1990s to contemporary contributions in robotic arc welding data infrastructure. His cumulative impact reflects a researcher consistently pushing the boundaries of how machines listen to and interpret their surroundings.
Research Focus
Key Achievements
Top Papers
- 1The LOCATA Challenge Data Corpus for Acoustic Source Localization and Tracking103 citations · 2018
- 2Acoustic Self-Awareness of Autonomous Systems in a World of Sounds22 citations · 2020
- 3
- 4Ego-noise reduction using a motor data-guided multichannel dictionary15 citations · 2016
- 5
- 6A generic data structure for the specific domain of robotic arc welding6 citations · 2018
- 7Informed Ego-noise Suppression Using Motor Data-driven Dictionaries6 citations · 2019
- 8
- 9Audio-motor integration for robot audition3 citations · 2018
- 10